Over the course of the three days, 20-22 May 2019, the Research Software
Engineering community and Microsoft worked together on a wide range of
projects focused on making researchers productive on Azure. Of the
proof-of-concept (POC) projects originally proposed three projects were
selected:

Deploying HPC on Azure using CycleCloud.

Push button deployment of BinderHub.

Documentation for deployment of The Littlest JupyterHub on Azure.

These POC’s were not only about providing training and creating reusable
resources for the RSE community, but also served to identify specific
barriers to adoption of Cloud in academic research institutions. Most of the
issues encountered where readily solved or worked around by the Azure
engineers present at the event. The Azure team were also in real-time contact
with the engineering teams internationally who develop the services. In the
case of CycleCloud, over the three days 20 separate issues were filed for
CycleCloud which were resolved either during or soon after the event.

Overall the feedback from the participants was great and the most common
question was when we were organising the next one :-)

This close engagement between the research community and Azure solution
architectures, supported by their engineering teams, that defines Research
Software Reactor.

Azure CycleCloud - cluster-in-the-cloud

During the sprint a deployment of HPC Services in the cloud was explored
using Azure
CycleCloud.
Azure CycleCloud is a tool for creating, managing, operating, and optimizing
HPC & Big Compute clusters in Azure. With Azure CycleCloud, users can
dynamically provision HPC Azure clusters and orchestrate data and jobs for
hybrid and cloud workflows. Azure CycleCloud provides alerting, monitoring,
and automatically scales HPC infrastructure to ensure your jobs run
efficiently at any scale. Azure CycleCloud offers advanced policy and
governance features such as: cost reporting and controls, usage reporting,
AD/LDAP integration, monitoring and alerting, and audit/event logging to give
users full control over who runs what, where, and at what cost within Azure.

The aim was to get CycleCloud working for people in personal or
organisational Azure Subscriptions. Objectives included:

Crib sheet for Research software engineers to use with Central IT around tenant and subscription access.

Provision Cycle Cloud for Research Software Engineers who typically are NOT subscription or Tenant Owners.

Instructions for the provisioning of Cycle Cloud Head Node deployment and config.

Thanks to the support of the Azure team at the sprint, and the engineering
teams who develop the services and provided us with real-time support, we
successfully deployed HPC clusters on Azure using CycleCloud and created
practical guidance for RSE’s to do this for themselves. As to be expected
when experimenting with new technologies we did run into some issues - over
the three days 20 separate issues were filed for CycleCloud. However, the
collaborative nature of the event meant that these issues were all resolved
during or shortly after the sprint.

RSEs wishing to use Cycle Cloud to deploy HPC cluster on campus should follow
the guidance at
https://github.com/research-software-reactor/cyclecloud/tree/master/arm-templates
and follow the initial setup guidance at
https://github.com/research-software-reactor/cyclecloud/blob/master/QuickStarts/SettingUpCycleCloud.md
You can follow the Quick Start tutorial at
https://github.com/research-software-reactor/cyclecloud/tree/master/QuickStartswhich
include Slurm Cluster Deployments.

Push button deployment of BinderHub on the Cloud

Sarah Gibson @drsarahlgibson led one of the sprint teams to create a push deployment of BinderHub for Azure. By the end of day 3 we had a functioning version but with some wrinkles. Thanks to the tenacity of the team all the wrinkles were ironed out in the weeks that followed. Sarah presented this work at RSEConUK 2019

☁️ How easy can it be to deploy your own Littlest JupyterHub on @Azure? I have created a Deploy to Azure button to help y'all:✨ Based on the official docs @ProjectJupyter✨ Reduces friction and gets you up running in a fraction of the timeCheck 👇🏽https://t.co/zzkxfceaD3